What’s important for countries competing in AI?
The potential for artificial intelligence (AI) to drive revenue and profit growth is enormous and it is widely accepted that AI has the potential to transform most industries. Governments and regulators play a critical role in adopting, or hindering, how technology can benefit their citizens. Research institutes and academic studies are also crucial for AI technology to develop.
A recent study carried out by Trung Ghi and Abhishek Srivastava, looked at the successes and failures of national AI strategies, and identified some key priorities for competing nations, including the role of government as well as private and public stakeholders.
The authors took a broad approach using a composite AI-readiness index (from Oxford Insights), rather than focusing on metrics such as patents and research publications. In this method that they chose, it factors in governance, skills and education, infrastructure and data, and government/public services. It revealed the top three countries to be Singapore, the UK, and Germany.
The study provided insights into what is, and is not, important for countries to compete successfully in AI. It found that leading countries have broad coverage across the impact metrics, with a stronger bias towards one or more, based on their context. For example, Germany is the strongest in the sample in private sector adoption of AI, driven by government support of AI-based industrial digitisation focused on SMEs and university AI R&D programs that have a strong bias for commercialisation.
Canada, the US, and Singapore all have strong scores in AI knowledge/skills capability. Israel, Singapore, and the US have well-defined AI governance frameworks with clear regulatory set-ups, well-established national and regional AI offices, and strong cyber-security and AI risk management practices.
Ensuring local and global AI talents were developed, attracted, and retained was central to national AI success, the analysis showed.
The US has almost 40% of the global AI talent, while Israel and Singapore have the highest scores for AI workforce and talent development from a tertiary education perspective. Countries such as Germany, the US, and Canada have been able to attract new AI talent into their economies over the last few years, while China, Israel, and Singapore have been unable to retain and grow their talent pools.
The authors suggested that based on their observations, a coordinated policy effort across academic institutions, public sector support, and incentives for private players has the highest likelihood of being successful in creating this virtuous cycle.
Path to success
The study showed that countries with AI strategies across multiple dimensions, including clearly defined national AI visions, investment in AI benefits, and protections for data usage, had higher likelihood of success compared to countries that focused on fewer dimensions. However, the path to success is not the same for all countries. The national context varies from country to country, and the way in which AI will transform industries and countries is not yet obvious. Each country has to create strong foundations to ensure that the future of AI is stable.
The advantages and disadvantages of AI in cloud computing
Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.
We take a look at some of the benefits and drawbacks of AI in cloud computing.
The benefits of AI in cloud computing
A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.
AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.
Improved data management
AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments.
Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks.
With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly.
The drawbacks of AI in cloud computing
Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.
IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity.
While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.